Multi-threshold image segmentation using histogram thresholding-Bayesian honey bee mating algorithm

Image thresholding is one of the most imperative practices to accomplish image segmentation, image compression and target recognition. This has been widely studied over the past few decades. However, the multilevel thresholding computationally takes more time when the threshold number increases. Hence, this paper proposes a honey bee mating-based algorithm (HBMA) based on Bayesian theorem and the characters of intensity images for image segmentation to save computation time. This kind of HBMA is called as Bayesian Honey Bee Mating Algorithm (BHBMA). Moreover, we adopt a population initialization strategy to make the search more efficient, according to the characters of multilevel thresholding in an image arranged from a low gray level to a high one. Extensive experiments have shown that BHBMA can deliver more effective and efficient results to be applied in complex image processing such as automatic target recognition, compared with state-of-the-art population-based thresholding methods.